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I'd like to know the minimum number of monthly data points required to do time series analysis with the seasonality effect in forecasting.

I read some articles & they were saying that 50 or 60 data points are sufficient. Is that the really minimum? Are there any trustful documentation which can be used as literature in this problem?

I'd be grateful if anyone can help.

Thank you.

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Hanke and Wichern, chapter 3, page 80 ( http://www.amazon.com/Business-Forecasting-Edition-John-Hanke/dp/0132301202 ) recommend a minimum 2xs to 6xs depending on the method (where s is the seasonal period, so s=12 for monthly data). 50 data points would be 50/12 = 4 years of data.

But it depends on the regularity of the data. If the seasonal pattern is quite regular, 3 years is OK.

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If you are going to perform the standard decomposition method, then it's the question of how many data points make the sample of each seasonal index, calculated as a geometric mean. So, how many data do you consider sufficient for a reasonable estimate of the mean value?

On the other hand, if you proceed with the ARIMA, it's the question of how many data points make a reasonable estimate of autocorrelation on the series reduced by a full season.

So think about it in this way. I have been working with time series analyses, 50-60 sounds reasonable to me.

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  • $\begingroup$ Actually i have 60 monthly data points only. Depending on that i have to proceed.Hence I'll have only 5 data points for each seasonal index. $\endgroup$ – Gayathri Oct 14 '14 at 15:45

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